{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel('./dataset/聚类数据.xlsx')\n",
    "data = df.loc[:,['DST','TDTC']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from skfuzzy.cluster import cmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "center, u, u0, d, jm, p, fpc = cmeans(data.T, m=2, c=3, error=0.00005, maxiter=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "label = u.T.argmax(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7fa9fbe43460>]"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZhUlEQVR4nO3df3Bd5Z3f8fdHP21LsmXrKgZbBssM643D2sZRHTZkKWFDYrxMIEmb4tmmtGHGaQda0ibTkN1pd7cznck0m2S7GyZbEihJl4EwAyy08QZYwi4lTQIyAWPHgI1jQLbXli2DLRtblvTtH/fIXMtX1vXVj6t7zuc1o9E9zznn3u89gz96eM45z1FEYGZm6VVT6QLMzGxqOejNzFLOQW9mlnIOejOzlHPQm5mlXF2lCygml8vF0qVLK12GmVnV2Lx588GIaC+2bkYG/dKlS+nu7q50GWZmVUPSG2Ot89CNmVnKOejNzFLOQW9mlnIOejOzlHPQm5mlnIPezCzlHPRmZimXmqCPCP78qR38/Wu9lS7FzGxGSU3QS+K7z+zi6VcOVLoUM7MZJTVBD5BraeTQsYFKl2FmNqOkKujbmho4ePRkpcswM5tRUhX0ueZGDvY76M3MCqUr6FsaPHRjZjZKqoK+ramRw8cHGBwarnQpZmYzRqqCPtfSSAT0HXev3sxsRLqCvqkBgINHHfRmZiPSFfQtjQAcOuYTsmZmI1IV9G0jPXpfeWNmdlqqgv50j77fQzdmZiNSFfQtjXU01NbQ6x69mdlpqQp6SeSaG9yjNzMrkKqgB2jz3bFmZmdIXdC7R29mdqZxg17SEklPS9ouaZuk25P2P5a0R9KLyc/6MfZfJ+lVSTsl3THZX2A09+jNzM5UV8I2g8CXIuIFSS3AZklPJuu+FRF/OtaOkmqBO4FrgR7geUmPRcSvJlr4WHLNjRzqHyAikDRVH2NmVjXG7dFHxL6IeCF5fRTYDiwu8f3XAjsjYldEDAAPADeUW2wpcs0NDAwNc+TE4FR+jJlZ1TivMXpJS4HLgV8kTbdJ2iLpHknzi+yyGHirYLmHMf5ISNooqVtSd29v+Y8DzDXnr6X38I2ZWV7JQS+pGXgI+GJEHAG+A1wCrAb2Ad8otluRtij2/hFxV0R0RURXe3t7qWWdZSTofULWzCyvpKCXVE8+5O+LiIcBImJ/RAxFxDDwXfLDNKP1AEsKljuAvRMr+dzamj0NgplZoVKuuhFwN7A9Ir5Z0H5hwWafArYW2f154FJJnZIagJuAxyZW8rm916N30JuZQWlX3VwJfA54WdKLSdsfABskrSY/FLMb+AKApEXA9yJifUQMSroNeByoBe6JiG2T+g1GmT+nHgl6PXRjZgaUEPQR8SzFx9o3jbH9XmB9wfKmsbadCnW1NSyY0+AevZlZInV3xkJ+nN5j9GZmeakM+pGbpszMLKVB72kQzMzek8qg98RmZmbvSWnQN3L05CAnTg1VuhQzs4pLadD7pikzsxGpDPq2Jk+DYGY2IpVBP/KQcPfozczSGvTJ0I179GZmqQ36fI++1z16M7N0Bv2s+lqaG+vcozczI6VBD54GwcxsRGqDPtfcyKFjDnozs9QGfVtTAwePeujGzCy1QZ9rcY/ezAzSHPRNDfQdG2BouOgjas3MMiO9Qd/SyHBA3zEP35hZtqU26E9Pg+DhGzPLuNQG/emJzXxC1swyLrVB39bsHr2ZGaQ46NtHpkE46qA3s2wbN+glLZH0tKTtkrZJuj1p/7qkVyRtkfSIpNYx9t8t6WVJL0rqnuT6xzR3dh31teKQT8aaWcaV0qMfBL4UEe8HrgBulbQCeBK4LCJWAq8BXz3He3w0IlZHRNeEKy6RJNqaGjnoHr2ZZdy4QR8R+yLiheT1UWA7sDginoiIwWSznwMdU1dmeXItDe7Rm1nmndcYvaSlwOXAL0at+jzwN2PsFsATkjZL2niO994oqVtSd29v7/mUNaa2pkZPbGZmmVdy0EtqBh4CvhgRRwra/5D88M59Y+x6ZUSsAa4jP+xzVbGNIuKuiOiKiK729vaSv8C55JobPVWxmWVeSUEvqZ58yN8XEQ8XtN8MXA/8fkQUnWsgIvYmvw8AjwBrJ1p0qXLNDfT2n2SM0szMMqGUq24E3A1sj4hvFrSvA74CfDIijo+xb5OklpHXwMeBrZNReClyzY0MDA7Tf3Jw/I3NzFKqlB79lcDngGuSSyRflLQe+DbQAjyZtP0lgKRFkjYl+y4EnpX0EvAc8KOI+PHkf43i2kbujvXwjZllWN14G0TEs4CKrNpUpG1kqGZ98noXsGoiBU7EyLNjD/afpDPXVKkyzMwqKrV3xsJ7PfpDvvLGzDIs1UF/ehoED92YWYalOujnN7lHb2aW6qCvr61h/px63zRlZpmW6qAH3zRlZpb6oG9rbnCP3swyLfVB7x69mWVdJoK+1z16M8uwDAR9A0dPDHJycKjSpZiZVUTqg/70s2M9fGNmGZX6oC+cBsHMLItSH/TvTYPgHr2ZZVPqg/69aRDcozezbEp90LtHb2ZZl/qgn9NQx5yGWo/Rm1lmpT7oId+r98RmZpZVmQj6XHOjnzJlZpmViaBva2r00I2ZZVYmgr69pcE9ejPLrEwEfa65kb5jJxkejkqXYmY27TIR9G1NDQwHHD7uXr2ZZc+4QS9piaSnJW2XtE3S7Un7AklPStqR/J4/xv7rJL0qaaekOyb7C5Qi15LMd3PMQW9m2VNKj34Q+FJEvB+4ArhV0grgDuCpiLgUeCpZPoOkWuBO4DpgBbAh2XdatTUl890c9QlZM8uecYM+IvZFxAvJ66PAdmAxcAPw/WSz7wM3Ftl9LbAzInZFxADwQLLftGpvyd8d62kQzCyLzmuMXtJS4HLgF8DCiNgH+T8GwPuK7LIYeKtguSdpK/beGyV1S+ru7e09n7LGNdKj33/kxKS+r5lZNSg56CU1Aw8BX4yII6XuVqSt6KUvEXFXRHRFRFd7e3upZZWkdU49y9qb+NtfHZjU9zUzqwYlBb2kevIhf19EPJw075d0YbL+QqBYivYASwqWO4C95ZdbHkl8Zk0Hz+3u481Dx6f7483MKqqUq24E3A1sj4hvFqx6DLg5eX0z8GiR3Z8HLpXUKakBuCnZb9p96vLFSPDQCz2V+Hgzs4oppUd/JfA54BpJLyY/64GvAddK2gFcmywjaZGkTQARMQjcBjxO/iTugxGxbQq+x7gWtc7mw5e08fAve4jwjVNmlh11420QEc9SfKwd4HeLbL8XWF+wvAnYVG6Bk+kzazr4Dw++xPO7D7O2c0GlyzEzmxaZuDN2xLrLLqCpoZaHNnv4xsyyI1NBP6ehjut+60J+9PI+3h0YqnQ5ZmbTIlNBD/DpNYvpPznIE7/6h0qXYmY2LTIX9Fd0trG4dTYPvbCn0qWYmU2LzAV9TY349JrFPLuj13fKmlkmZC7oAT69poPhgEd+6V69maVfJoO+M9fEmotaeWizr6k3s/TLZNADfOaDHew40M/Le96pdClmZlMqs0F//cpFNNTV8LBPyppZymU26OfNrufaFQt59MU9DAwOV7ocM7Mpk9mgB/gnazo4fPwUT7/q6YvNLL0yHfS/c2mOXHOjp0Qws1TLdNDX1dZw4+pFPP3qAfr84HAzS6lMBz3Ap9Ys5tRQ8NT2/ZUuxcxsSmQ+6H/zgrnU14rXe49VuhQzsymR+aCvrREXtzWxq7e/0qWYmU2JzAc95O+U/fVB9+jNLJ0c9MCyXBNvHDrO0LCnQzCz9HHQA8vamxgYGmbv2+9WuhQzs0nnoAc6c80AvO5xejNLIQc9+TF6wOP0ZpZKdeNtIOke4HrgQERclrT9EFiebNIKvB0Rq4vsuxs4CgwBgxHRNSlVT7JccwMtjXUOejNLpXGDHrgX+Dbwg5GGiPhnI68lfQM411y/H42Ig+UWOB0ksazdV96YWTqNO3QTEc8AfcXWSRLwWeD+Sa5r2nXmmtjlm6bMLIUmOkb/O8D+iNgxxvoAnpC0WdLGc72RpI2SuiV19/b2TrCs89eZa2bvO+9y4tTQtH+2mdlUmmjQb+DcvfkrI2INcB1wq6SrxtowIu6KiK6I6Gpvb59gWeevs72JCNh9yL16M0uXsoNeUh3waeCHY20TEXuT3weAR4C15X7eVFs2cuWNh2/MLGUm0qP/GPBKRBSdzF1Sk6SWkdfAx4GtE/i8KTVyieUun5A1s5QZN+gl3Q/8DFguqUfSLcmqmxg1bCNpkaRNyeJC4FlJLwHPAT+KiB9PXumTq6mxjoVzG31C1sxSZ9zLKyNiwxjt/7JI215gffJ6F7BqgvVNq/zkZr471szSxXfGFujMNftaejNLHQd9gWW5Jg4fP8VhP1bQzFLEQV9gWXty5Y0vsTSzFHHQFzh95Y1PyJpZijjoCyxZMIfaGvmErJmlioO+QH1tDRctmOMTsmaWKg76UTy5mZmljYN+lGW5JnYfOsawnx9rZinhoB+ls72JE6eG2XfkRKVLMTObFA76UTo9uZmZpYyDfpRlyYPCfeWNmaWFg36UhXMbmdNQ61kszSw1HPSjSPKVN2aWKg76IvKzWDrozSwdHPRFLMs10XP4OCcH/fxYM6t+DvoiOtubGA54q+94pUsxM5swB30RI1fevO5xejNLAQd9EUtHrqX3OL2ZpYCDvoh5s+vJNTf4pikzSwUH/Rh85Y2ZpYWDfgyduSZ2+e5YM0uBcYNe0j2SDkjaWtD2x5L2SHox+Vk/xr7rJL0qaaekOyaz8Km2rL2Zg/0DvPPuqUqXYmY2IaX06O8F1hVp/1ZErE5+No1eKakWuBO4DlgBbJC0YiLFTqeRyc12e/jGzKrcuEEfEc8AfWW891pgZ0TsiogB4AHghjLepyKW+cobM0uJiYzR3yZpSzK0M7/I+sXAWwXLPUlbUZI2SuqW1N3b2zuBsibHRW1zkPDkZmZW9coN+u8AlwCrgX3AN4psoyJtYz62KSLuioiuiOhqb28vs6zJ01hXS8f82ezq9QlZM6tuZQV9ROyPiKGIGAa+S36YZrQeYEnBcgewt5zPq5RluWYP3ZhZ1Ssr6CVdWLD4KWBrkc2eBy6V1CmpAbgJeKycz6uUkWvpI/z8WDOrXnXjbSDpfuBqICepB/gj4GpJq8kPxewGvpBsuwj4XkSsj4hBSbcBjwO1wD0RsW0qvsRUWdbexPGBIQ4cPcnCubMqXY6ZWVnGDfqI2FCk+e4xtt0LrC9Y3gScdelltRi5xPL1A/0OejOrWr4z9hx+84K5AGzbe6TClZiZlc9Bfw7tLY0smjeLl3rernQpZmZlc9CPY2VHK1t63ql0GWZmZXPQj2Plknm82Xecw8cGKl2KmVlZHPTjWNXRCsCWPe7Vm1l1ctCP47LF8wDY8tbblS3EzKxMDvpxzJtdz7JcEy95nN7MqpSDvgQrO+bx8p63K12GmVlZHPQlWNnRyv4jJ9l/5ESlSzEzO28O+hKsWpIfp3/J4/RmVoUc9CVYceE8amvk6+nNrCo56Eswu6GW31jY4jtkzawqOehLtKpjHi/vecdTFptZ1XHQl2hlRytvHz/Fm33HK12Kmdl5cdCXaGVHckLW4/RmVmUc9CVafkELDXU1vkPWzKqOg75E9bU1rLhwrq+8MbOq46A/D6s65rF17zsMDfuErJlVDwf9eVjZ0crxgSF2HuivdClmZiVz0J+H03fI+np6M6siDvrzsCzXTHNjHVsc9GZWRcYNekn3SDogaWtB29clvSJpi6RHJLWOse9uSS9LelFS9yTWXRE1NeKyxT4ha2bVpZQe/b3AulFtTwKXRcRK4DXgq+fY/6MRsToiusorcWZZ1dHK9n1HODk4VOlSzMxKMm7QR8QzQN+oticiYjBZ/DnQMQW1zUgrO1o5NRS8su9opUsxMyvJZIzRfx74mzHWBfCEpM2SNp7rTSRtlNQtqbu3t3cSypoaI3fIepzezKrFhIJe0h8Cg8B9Y2xyZUSsAa4DbpV01VjvFRF3RURXRHS1t7dPpKwp1TF/NguaGjwVgplVjbKDXtLNwPXA78cYUzpGxN7k9wHgEWBtuZ83U0hiZcc89+jNrGqUFfSS1gFfAT4ZEUWnc5TUJKll5DXwcWBrsW2rzcqOVnYe6OfYycHxNzYzq7BSLq+8H/gZsFxSj6RbgG8DLcCTyaWTf5lsu0jSpmTXhcCzkl4CngN+FBE/npJvMc1WdcxjOGDrHg/fmNnMVzfeBhGxoUjz3WNsuxdYn7zeBayaUHUz1MqOVgC29LzDh5a1VbYYM7Nx+M7YMrS3NLJo3ixPhWBmVcFBX6aVHa2+Q9bMqoKDvkwrl8zjzb7jvH18oNKlmJmdk4O+TB/qzI/NP/LLPRWuxMzs3Bz0ZfrgxfP58CVt3Pn0Tl9maWYzmoN+Ar78ieUc7B/gf/7015UuxcxsTA76CVhz0XyuXbGQ//HMLo/Vm9mM5aCfoC9/fDn9Jwf5zt+/XulSzMyKctBP0PILWrhx9WK+//92s//IiUqXY2Z2Fgf9JPj3H/sNBoeCv/jJjkqXYmZ2Fgf9JLiobQ43rV3CA8+9xRuHjlW6HDOzMzjoJ8m/u+ZS6mrFt558rdKlmJmdwUE/Sd43dxY3f3gpj760l1f+4UilyzEzO81BP4n+zT++hObGOv70cffqzWzmcNBPotY5DXzhqmX87fb9bH7jcKXLMTMDHPST7l9d2UmuuYGvP/4KYzxh0cxsWjnoJ1lTYx3/9ppL+fmuPj5/7/P0Hj1Z6ZLMLOMc9FPgX/z2xfzJJz/AT18/xLo/e4afvLK/0iWZWYY56KeAJG7+8FL+920fob2lkc/f281/fnQrJ04NVbo0M8sgB/0UWn5BC39965Xc8pFOfvCzN7j+L55l214/lcrMppeDforNqq/lP12/gv91y1qOvHuKG+/8KX/+1A527D/K8LBP1prZ1NN4V4ZIuge4HjgQEZclbQuAHwJLgd3AZyPirOsJJa0D/jtQC3wvIr5WSlFdXV3R3d1d+reoEn3HBvjqw1t4fFt+zL51Tj1dF8/ngxcv4B8tnc9vdcyjsa62wlWaWTWStDkiuoquKyHorwL6gR8UBP1/A/oi4muS7gDmR8RXRu1XC7wGXAv0AM8DGyLiV+MVnNagH/Hrg8d4fncf3bv76N59mF0H8/PjNNTVsHxhCwuaGljQ1MD8OQ0saKqndU5+ee6sehrqamisqxn1u5aG2hpqaqCupobaGlFbI2qUP19gZul3rqCvG2/niHhG0tJRzTcAVyevvw/8HfCVUdusBXZGxK6kiAeS/cYN+rTrzDXRmWvis11LADjYf5LNbxyme3cfr+7v5/DxAV7v7efwsQGODUzsBG5djahJQr9GokZCp1/n/xAIyP89GFkHSl7nW8/8gyFB4d+P/DtwxvaFzth3dIEq+vK8zJQ/ZjOjCqtm8+c08OC//u1Jf99xg34MCyNiH0BE7JP0viLbLAbeKljuAT401htK2ghsBLjooovKLKs65Zob+cQHLuATH7jgrHUnB4d4+/gp+o4NcPTEIAODw5wcHEp+D7+3PBQMDweDw8FwBINDwVAEg0PDDAdE5NuHA4YjiICh4SDIvw4g/z93+eXh5P/0zlwHQdLAyHLye2T7UfVHkW3fWxdjrivZDDnNETOlEKtqc2fVT8n7lhv0pSjWwRnzX0NE3AXcBfmhm6kqqto01tWycG4tC+fOqnQpZlalyr3qZr+kCwGS3weKbNMDLClY7gD2lvl5ZmZWpnKD/jHg5uT1zcCjRbZ5HrhUUqekBuCmZD8zM5tG4wa9pPuBnwHLJfVIugX4GnCtpB3kr6r5WrLtIkmbACJiELgNeBzYDjwYEdum5muYmdlYSrnqZsMYq363yLZ7gfUFy5uATWVXZ2ZmE+Y7Y83MUs5Bb2aWcg56M7OUc9CbmaXcuHPdVIKkXuCNMnfPAQcnsZw08DE5m4/J2XxMzlZNx+TiiGgvtmJGBv1ESOoea2KfrPIxOZuPydl8TM6WlmPioRszs5Rz0JuZpVwag/6uShcwA/mYnM3H5Gw+JmdLxTFJ3Ri9mZmdKY09ejMzK+CgNzNLudQEvaR1kl6VtDN5jm0mSbpH0gFJWwvaFkh6UtKO5Pf8StY43SQtkfS0pO2Stkm6PWnP7HGRNEvSc5JeSo7JnyTtmT0mIyTVSvqlpP+TLFf9MUlF0CcPIr8TuA5YAWyQtKKyVVXMvcC6UW13AE9FxKXAU8lylgwCX4qI9wNXALcm/31k+bicBK6JiFXAamCdpCvI9jEZcTv5qdVHVP0xSUXQU/Ag8ogYAEYeRJ45EfEM0Deq+QbyD3En+X3jdNZUaRGxLyJeSF4fJf+PeDEZPi6R158s1ic/QYaPCYCkDuD3gO8VNFf9MUlL0Bd7EPniCtUyE53xMHeg2MPcM0HSUuBy4Bdk/LgkQxQvkn8U6JMRkfljAvwZ8B+B4YK2qj8maQn683oQuWWTpGbgIeCLEXGk0vVUWkQMRcRq8s9zXivpsgqXVFGSrgcORMTmStcy2dIS9H4Q+bmV8jD3VJNUTz7k74uIh5PmzB8XgIh4G/g78ud2snxMrgQ+KWk3+eHfayT9FSk4JmkJej+I/NxKeZh7akkScDewPSK+WbAqs8dFUruk1uT1bOBjwCtk+JhExFcjoiMilpLPkJ9ExD8nBcckNXfGSlpPfnytFrgnIv5rZSuqjORh7leTn151P/BHwF8DDwIXAW8C/zQiRp+wTS1JHwH+L/Ay7429/gH5cfpMHhdJK8mfWKwl3+F7MCL+i6Q2MnpMCkm6GvhyRFyfhmOSmqA3M7Pi0jJ0Y2ZmY3DQm5mlnIPezCzlHPRmZinnoDczSzkHvZlZyjnozcxS7v8Da4RLytb/2noAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list(range(len(jm))), jm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x7fa9fac55d60>"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = data.T[0], data.T[1]\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x, y, marker='.', c=label, cmap=\"cool_r\")\n",
    "cx, cy = center.T\n",
    "plt.scatter(cx, cy, marker='*', c=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "d = np.fromfile('dataset/s202012272243_02.dat', dtype='float32')\n",
    "df = pd.DataFrame(d)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "        0\n0     0.0\n1     0.0\n2     0.0\n3     0.0\n4     0.0\n...   ...\n2970  0.0\n2971  0.0\n2972  0.0\n2973  0.0\n2974  0.0\n\n[2975 rows x 1 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2970</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2971</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2972</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2973</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2974</th>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2975 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
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    "df"
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   "outputs": [],
   "source": [
    "df.to_excel('data.xlsx')"
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     "name": "#%%\n"
    }
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   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
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